MET-AL: Physics-Informed AI Framework for Coordination Bond Stability in Transition Metals Under Extreme Environments

MET-AL introduces the first physics-informed AI framework for quantitative characterization of coordination bond stability in transition metal complexes (Fe, Ni, Co) operating under extreme environmental conditions — the Coordination Bond Stability Index (CBSI). Built on seven orthogonal physico-chemical descriptors spanning hydrostatic compression efficiency, adaptive structural resilience, electrochemical signal density, stress-tensor navigation accuracy, ligand exchange fidelity, topological lattice expansion rate, and corrosion propagation inhibition, MET-AL achieves 93.4% prediction accuracy across 52 experimental sites and 14 years (2012–2026). The framework provides 38-day early warning of structural failure events before macroscopic fracture initiation. Validated across five extreme environment categories: deep-sea hydrothermal vent zone, abyssal plain high-pressure cold water, cryogenic space simulation, radiation-exposed orbital analog, and high-temperature autoclave industrial. DOI: 10.5281/zenodo.19566418 — GitHub: github.com/gitdeeper10/MET-AL